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Artificial Intelligence-Based ECG Diagnosis of Myocardial Infarction in High-Risk Emergency Department Patients

22 Pages Posted: 8 Jun 2021

See all articles by Stefan Gustafsson

Stefan Gustafsson

Uppsala University - Clinical Epidemiology Unit

Daniel Gedon

Uppsala University - Department of Information Technology

Erik Lampa

Uppsala University - Clinical Epidemiology Unit

Antônio H. Ribeiro

Uppsala University - Division of Systems and Control

Martin J. Holzmann

Karolinska Institutet

Thomas B. Schön

Uppsala University - Division of Systems and Control

Johan Sundstrom

Uppsala University - Clinical Epidemiology Unit

More...

Abstract

Background: Myocardial infarctions are often missed in the emergency department. Deep learning models have shown promise in ECG classification in managed settings. We postulate that deep learning models can be useful for ECG myocardial infarction discrimination also in real-world emergency department patients with a high suspicion of an acute coronary syndrome.

Methods: We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained and were admitted to a coronary care unit. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs were ECG, age and sex; output label was non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status as registered in the SWEDEHEART registry by the discharging coronary care unit physician. We used an ensemble of five models.

Findings: Of the included 12,311 coronary care unit admissions, 3,993 were recorded with an NSTEMI, 1,340 a STEMI, and 6,978 had not had a myocardial infarction. In a random test set, our model could discriminate STEMIs with fair precision, with a C-statistic of 0.85 and a Brier score of 0.10. Discrimination of NSTEMIs was poorer, with a C-statistic of 0.76 and a Brier score of 0.18. The performance was similar in a temporal test set that did not overlap in time with the training set.

Interpretation: We developed and validated a deep learning model with human-level performance in classifying NSTEMI and STEMI on the presenting ECG of a real-world sample of emergency department patients with a high suspicion of an acute coronary syndrome. Deep learning models for ECG decision support could be valuable in the emergency department.

Funding: The Kjell and Märta Beijer Foundation, Anders Wiklöf, the Wallenberg AI, Autonomous
Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg
Foundation, and Uppsala University.

Declaration of Interest: JS reports stock ownership in companies providing services to Itrim, Amgen, Janssen, Novo Nordisk, Eli Lilly, Boehringer, Bayer, Pfizer and AstraZeneca, outside the submitted work. No other authors have anything to declare.

Ethical Approval: The study was approved by the Swedish Ethical Review Agency, application number 2020-01654.

Suggested Citation

Gustafsson, Stefan and Gedon, Daniel and Lampa, Erik and Ribeiro, Antônio H. and Holzmann, Martin J. and Schön, Thomas B. and Sundstrom, Johan, Artificial Intelligence-Based ECG Diagnosis of Myocardial Infarction in High-Risk Emergency Department Patients. Available at SSRN: https://ssrn.com/abstract=3857655 or http://dx.doi.org/10.2139/ssrn.3857655

Stefan Gustafsson

Uppsala University - Clinical Epidemiology Unit

Uppsala
Sweden

Daniel Gedon

Uppsala University - Department of Information Technology ( email )

Division of Visual Information and Interaction
Box 337
UPPSALA, 751 05
Sweden

Erik Lampa

Uppsala University - Clinical Epidemiology Unit ( email )

Uppsala
Sweden

Antônio H. Ribeiro

Uppsala University - Division of Systems and Control ( email )

Box 513
Uppsala, 751 20
Sweden

Martin J. Holzmann

Karolinska Institutet

Granits väg 4
Section for Integrative Physiology
Solna, 17171
Sweden

Thomas B. Schön

Uppsala University - Division of Systems and Control ( email )

Box 513
Uppsala, 751 20
Sweden

Johan Sundstrom (Contact Author)

Uppsala University - Clinical Epidemiology Unit ( email )

Uppsala
Sweden

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